This functionality is available only if you purchase the Partitioning option.

Introduction to Partitioning

Partitioning addresses key issues in supporting very large tables and indexes by letting you decompose them into smaller and more manageable pieces called partitions. SQL queries and DML statements do not need to be modified in order to access partitioned tables. However, after partitions are defined, DDL statements can access and manipulate individuals partitions rather than entire tables or indexes. This is how partitioning can simplify the manageability of large database objects. Also, partitioning is entirely transparent to applications.

Each partition of a table or index must have the same logical attributes, such as column names, datatypes, and constraints, but each partition can have separate physical attributes such as pctfree, pctused, and tablespaces.

Partitioning is useful for many different types of applications, particularly applications that manage large volumes of data. OLTP systems often benefit from improvements in manageability and availability, while data warehousing systems benefit from performance and manageability.

Note:

All partitions of a partitioned object must reside in tablespaces of a single block size.

Partitioning enables data management operations such data loads, index creation and rebuilding, and backup/recovery at the partition level, rather than on the entire table. This results in significantly reduced times for these operations.

Partitioning improves query performance. In many cases, the results of a query can be achieved by accessing a subset of partitions, rather than the entire table. For some queries, this technique (called partitionpruning) can provide order-of-magnitude gains in performance.

Partitioning can significantly reduce the impact of scheduled downtime for maintenance operations.

Partition independence for partition maintenance operations lets you perform concurrent maintenance operations on different partitions of the same table or index. You can also run concurrent SELECT and DML operations against partitions that are unaffected by maintenance operations.

Partitioning increases the availability of mission-critical databases if critical tables and indexes are divided into partitions to reduce the maintenance windows, recovery times, and impact of failures.

Partitioning can be implemented without requiring any modifications to your applications. For example, you could convert a nonpartitioned table to a partitioned table without needing to modify any of the SELECT statements or DML statements which access that table. You do not need to rewrite your application code to take advantage of partitioning.

Partition Key

Each row in a partitioned table is unambiguously assigned to a single partition. The partition key is a set of one or more columns that determines the partition for each row. Oracle automatically directs insert, update, and delete operations to the appropriate partition through the use of the partition key. A partition key:

Consists of an ordered list of 1 to 16 columns

Cannot contain a LEVEL, ROWID, or MLSLABEL pseudocolumn or a column of type ROWID

Can contain columns that are NULLable

Partitioned Tables

Tables can be partitioned into up to 64,000 separate partitions. Any table can be partitioned except those tables containing columns with LONG or LONGRAW datatypes. You can, however, use tables containing columns with CLOB or BLOB datatypes.

Note:

To reduce disk use and memory use (specifically, the buffer cache), you can store tables and partitioned tables in a compressed format inside the database. This often leads to a better scaleup for read-only operations. Table compression can also speed up query execution. There is, however, a slight cost in CPU overhead.

Partitioned Index-Organized Tables

You can partition index-organized tables by range, list, or hash. Partitioned index-organized tables are very useful for providing improved manageability, availability, and performance for index-organized tables. In addition, data cartridges that use index-organized tables can take advantage of the ability to partition their stored data. Common examples of this are the Image and interMedia cartridges.

For partitioning an index-organized table:

Partition columns must be a subset of primary key columns

Secondary indexes can be partitioned — locally and globally

OVERFLOW data segments are always equipartitioned with the table partitions

Range Partitioning

Range partitioning maps data to partitions based on ranges of partition key values that you establish for each partition. It is the most common type of partitioning and is often used with dates. For example, you might want to partition sales data into monthly partitions.

When using range partitioning, consider the following rules:

Each partition has a VALUESLESSTHAN clause, which specifies a noninclusive upper bound for the partitions. Any values of the partition key equal to or higher than this literal are added to the next higher partition.

All partitions, except the first, have an implicit lower bound specified by the VALUESLESSTHAN clause on the previous partition.

A MAXVALUE literal can be defined for the highest partition. MAXVALUE represents a virtual infinite value that sorts higher than any other possible value for the partition key, including the null value.

A typical example is given in the following section. The statement creates a table (sales_range) that is range partitioned on the sales_date field.

List Partitioning

List partitioning enables you to explicitly control how rows map to partitions. You do this by specifying a list of discrete values for the partitioning key in the description for each partition. This is different from range partitioning, where a range of values is associated with a partition and from hash partitioning, where a hash function controls the row-to-partition mapping. The advantage of list partitioning is that you can group and organize unordered and unrelated sets of data in a natural way.

The details of list partitioning can best be described with an example. In this case, let's say you want to partition a sales table by region. That means grouping states together according to their geographical location as in the following example.

A row is mapped to a partition by checking whether the value of the partitioning column for a row falls within the set of values that describes the partition. For example, the rows are inserted as follows:

Unlike range and hash partitioning, multicolumn partition keys are not supported for list partitioning. If a table is partitioned by list, the partitioning key can only consist of a single column of the table.

The DEFAULT partition enables you to avoid specifying all possible values for a list-partitioned table by using a default partition, so that all rows that do not map to any other partition do not generate an error.

Hash Partitioning

Hash partitioning enables easy partitioning of data that does not lend itself to range or list partitioning. It does this with a simple syntax and is easy to implement. It is a better choice than range partitioning when:

You do not know beforehand how much data maps into a given range

The sizes of range partitions would differ quite substantially or would be difficult to balance manually

Range partitioning would cause the data to be undesirably clustered

Performance features such as parallel DML, partition pruning, and partition-wise joins are important

The concepts of splitting, dropping or merging partitions do not apply to hash partitions. Instead, hash partitions can be added and coalesced.

Hash Partitioning Example

The preceding statement creates a table sales_hash, which is hash partitioned on salesman_id field. The tablespace names are ts1, ts2, ts3, and ts4. With this syntax, we ensure that we create the partitions in a round-robin manner across the specified tablespaces.

Composite Partitioning

Composite partitioning partitions data using the range method, and within each partition, subpartitions it using the hash or list method. Composite range-hash partitioning provides the improved manageability of range partitioning and the data placement, striping, and parallelism advantages of hash partitioning. Composite range-list partitioning provides the manageability of range partitioning and the explicit control of list partitioning for the subpartitions.

Composite partitioning supports historical operations, such as adding new range partitions, but also provides higher degrees of parallelism for DML operations and finer granularity of data placement through subpartitioning.

This statement creates a table sales_composite that is range partitioned on the sales_date field and hash subpartitioned on salesman_id. When you use a template, Oracle names the subpartitions by concatenating the partition name, an underscore, and the subpartition name from the template. Oracle places this subpartition in the tablespace specified in the template. In the previous statement, sales_jan2000_sp1 is created and placed in tablespace ts1 while sales_jan2000_sp4 is created and placed in tablespace ts4. In the same manner, sales_apr2000_sp1 is created and placed in tablespace ts1 while sales_apr2000_sp4 is created and placed in tablespace ts4. Figure 18-4 offers a graphical view of the previous example.

(
PARTITION janfeb_2000 VALUES LESS THAN (TO_DATE('1-MAR-2000','DD-MON-YYYY')),
PARTITION marapr_2000 VALUES LESS THAN (TO_DATE('1-MAY-2000','DD-MON-YYYY')),
PARTITION mayjun_2000 VALUES LESS THAN (TO_DATE('1-JUL-2000','DD-MON-YYYY'))
);

This statement creates a table bimonthly_regional_sales that is range partitioned on the txn_date field and list subpartitioned on state. When you use a template, Oracle names the subpartitions by concatenating the partition name, an underscore, and the subpartition name from the template. Oracle places this subpartition in the tablespace specified in the template. In the previous statement, janfeb_2000_east is created and placed in tablespace ts1 while janfeb_2000_central is created and placed in tablespace ts3. In the same manner, mayjun_2000_east is placed in tablespace ts1 while mayjun_2000_central is placed in tablespace ts3. Figure 18-5 offers a graphical view of the table bimonthly_regional_sales and its 9 individual subpartitions.

When to Partition a Table

Here are some suggestions for when to partition a table:

Tables greater than 2GB should always be considered for partitioning.

Tables containing historical data, in which new data is added into the newest partition. A typical example is a historical table where only the current month's data is updatable and the other 11 months are read only.

Overview of Partitioned Indexes

Just like partitioned tables, partitioned indexes improve manageability, availability, performance, and scalability. They can either be partitioned independently (global indexes) or automatically linked to a table's partitioning method (local indexes). In general, you should use global indexes for OLTP applications and local indexes for data warehousing or DSS applications. Also, whenever possible, you should try to use local indexes because they are easier to manage. When deciding what kind of partitioned index to use, you should consider the following guidelines in order:

If the table partitioning column is a subset of the index keys, use a local index. If this is the case, you are finished. If this is not the case, continue to guideline 2.

If the index is unique, use a global index. If this is the case, you are finished. If this is not the case, continue to guideline 3.

If your priority is manageability, use a local index. If this is the case, you are finished. If this is not the case, continue to guideline 4.

If the application is an OLTP one and users need quick response times, use a global index. If the application is a DSS one and users are more interested in throughput, use a local index.

Local Partitioned Indexes

Local partitioned indexes are easier to manage than other types of partitioned indexes. They also offer greater availability and are common in DSS environments. The reason for this is equipartitioning: each partition of a local index is associated with exactly one partition of the table. This enables Oracle to automatically keep the index partitions in sync with the table partitions, and makes each table-index pair independent. Any actions that make one partition's data invalid or unavailable only affect a single partition.

Local partitioned indexes support more availability when there are partition or subpartition maintenance operations on the table. A type of index called a local nonprefixed index is very useful for historical databases. In this type of index, the partitioning is not on the left prefix of the index columns.

You cannot explicitly add a partition to a local index. Instead, new partitions are added to local indexes only when you add a partition to the underlying table. Likewise, you cannot explicitly drop a partition from a local index. Instead, local index partitions are dropped only when you drop a partition from the underlying table.

A local index can be unique. However, in order for a local index to be unique, the partitioning key of the table must be part of the index's key columns. Unique local indexes are useful for OLTP environments.

Global Partitioned Indexes

Global Range Partitioned Indexes

Global range partitioned indexes are flexible in that the degree of partitioning and the partitioning key are independent from the table's partitioning method. They are commonly used for OLTP environments and offer efficient access to any individual record.

The highest partition of a global index must have a partition bound, all of whose values are MAXVALUE. This ensures that all rows in the underlying table can be represented in the index. Global prefixed indexes can be unique or nonunique.

You cannot add a partition to a global index because the highest partition always has a partition bound of MAXVALUE. If you wish to add a new highest partition, use the ALTERINDEXSPLITPARTITION statement. If a global index partition is empty, you can explicitly drop it by issuing the ALTERINDEXDROPPARTITION statement. If a global index partition contains data, dropping the partition causes the next highest partition to be marked unusable. You cannot drop the highest partition in a global index.

Global Hash Partitioned Indexes

Global hash partitioned indexes improve performance by spreading out contention when the index is monotonically growing. In other words, most of the index insertions occur only on the right edge of an index.

Maintenance of Global Partitioned Indexes

By default, the following operations on partitions on a heap-organized table mark all global indexes as unusable:

ADD (HASH)
COALESCE (HASH)
DROP
EXCHANGE
MERGE
MOVE
SPLIT
TRUNCATE

These indexes can be maintained by appending the clause UPDATE INDEXES to the SQL statements for the operation. The two advantages to maintaining global indexes:

The index remains available and online throughout the operation. Hence no other applications are affected by this operation.

You can create bitmap indexes on partitioned tables, with the restriction that the bitmap indexes must be local to the partitioned table. They cannot be global indexes.

Global indexes can be unique. Local indexes can only be unique if the partitioning key is a part of the index key.

Using Partitioned Indexes in OLTP Applications

Here are a few guidelines for OLTP applications:

Global indexes and unique, local indexes provide better performance than nonunique local indexes because they minimize the number of index partition probes.

Local indexes offer better availability when there are partition or subpartition maintenance operations on the table.

Hash-partitioned global indexes offer better performance by spreading out contention when the index is monotonically growing. In other words, most of the index insertions occur only on the right edge of an index.

Using Partitioned Indexes in Data Warehousing and DSS Applications

Here are a few guidelines for data warehousing and DSS applications:

Local indexes are preferable because they are easier to manage during data loads and during partition-maintenance operations.

Local indexes can improve performance because many index partitions can be scanned in parallel by range queries on the index key.

Partitioned Indexes on Composite Partitions

Here are a few points to remember when using partitioned indexes on composite partitions:

Subpartitioned indexes are always local and stored with the table subpartition by default.

Tablespaces can be specified at either index or index subpartition levels.

Partitioning to Improve Performance

Partitioning can help you improve performance and manageability. Some topics to keep in mind when using partitioning for these reasons are:

Partition Pruning

The Oracle database server explicitly recognizes partitions and subpartitions. It then optimizes SQL statements to mark the partitions or subpartitions that need to be accessed and eliminates (prunes) unnecessary partitions or subpartitions from access by those SQL statements. In other words, partition pruning is the skipping of unnecessary index and data partitions or subpartitions in a query.

For each SQL statement, depending on the selection criteria specified, unneeded partitions or subpartitions can be eliminated. For example, if a query only involves March sales data, then there is no need to retrieve data for the remaining eleven months. Such intelligent pruning can dramatically reduce the data volume, resulting in substantial improvements in query performance.

If the optimizer determines that the selection criteria used for pruning are satisfied by all the rows in the accessed partition or subpartition, it removes those criteria from the predicate list (WHERE clause) during evaluation in order to improve performance. However, the optimizer cannot prune partitions if the SQL statement applies a function to the partitioning column (with the exception of the TO_DATE function). Similarly, the optimizer cannot use an index if the SQL statement applies a function to the indexed column, unless it is a function-based index.

Pruning can eliminate index partitions even when the underlying table's partitions cannot be eliminated, but only when the index and table are partitioned on different columns. You can often improve the performance of operations on large tables by creating partitioned indexes that reduce the amount of data that your SQL statements need to access or modify.

Equality, range, LIKE, and IN-list predicates are considered for partition pruning with range or list partitioning, and equality and IN-list predicates are considered for partition pruning with hash partitioning.

Partition Pruning Example

We have a partitioned table called cust_orders. The partition key for cust_orders is order_date. Let us assume that cust_orders has six months of data, January to June, with a partition for each month of data. If the following query is run:

SELECT SUM(value)
FROM cust_orders
WHERE order_date BETWEEN '28-MAR-98' AND '23-APR-98';

Partition pruning is achieved by:

First, partition elimination of January, February, May, and June data partitions. Then either:

An index scan of the March and April data partition due to high index selectivity

or

A full scan of the March and April data partition due to low index selectivity

Partition-wise Joins

A partition-wise join is a join optimization for joining two tables that are both partitioned along the join column(s). With partition-wise joins, the join operation is broken into smaller joins that are performed sequentially or in parallel. Another way of looking at partition-wise joins is that they minimize the amount of data exchanged among parallel slaves during the execution of parallel joins by taking into account data distribution.

Parallel DML

Parallel execution dramatically reduces response time for data-intensive operations on large databases typically associated with decision support systems and data warehouses. In addition to conventional tables, you can use parallel query and parallel DML with range- and hash-partitioned tables. By doing so, you can enhance scalability and performance for batch operations.

The semantics and restrictions for parallel DML sessions are the same whether you are using index-organized tables or not.